Systems and methods for detecting, monitoring, and mitigating the presence of a drone using frequency hopping

ABSTRACT

Systems and methods for detecting, monitoring, and mitigating the presence of a drone are provided herein. In one aspect, a system for detecting presence of a one or more drones includes a radio-frequency (RF) receiver configured to receive an RF signal transmitted between a drone and a controller. The system can further include a processor and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the at least one processor to receive a set of samples from the RF receiver for a time interval, the set of samples comprising samples of the first RF signal, obtain a parameter model of the first frequency hopping parameters, and fit the parameter model to the set of samples.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional Application No.62/921,033, filed May 29, 2019, which is hereby incorporated byreference in its entirety.

BACKGROUND Technological Field

The systems and methods disclosed herein are directed to detecting,monitoring, and mitigating the presence of a drone. More particularly,the systems and methods can be used to detect the presence of aplurality of drones employing radio-frequency (RF) communications.

Description of the Related Technology

In recent years, Unmanned Aircraft Systems (UAS), more commonly known asdrones, have been used extensively in a large number of exciting andcreative applications, ranging from aerial photography, agriculture,product delivery, infrastructure inspection, aerial light shows, andhobbyist drone racing. Despite the usefulness of drones in manyapplications they also pose increasing security, safety, and privacyconcerns. Drones are being used to smuggle weapons and drugs acrossborders. The use of drones near airports presents safety concerns, whichmay require airports to shut down until the surrounding airspace issecured. Drones are also used as a tool of corporate and state espionageactivities. Thus, there is demand for an effective Counter-UnmannedAircraft System (CUAS) solution to detect and monitor drones andmitigate the threat of drones when necessary.

SUMMARY

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for thedesirable attributes disclosed herein.

In one aspect, there is provided a system for detecting presence of aone or more drones, the system comprising: a radio-frequency (RF)receiver configured to receive a first RF signal transmitted between afirst drone and a first drone controller, the first RF signal includinga first frequency hopping sequence defined by a plurality of firstfrequency hopping parameters; a processor; and a computer-readablememory in communication with the processor and having stored thereoncomputer-executable instructions to cause the at least one processor to:receive a sequence set of samples from the RF receiver for a timeinterval, the set of samples comprising samples of the first RF signal,obtain a parameter model of the first frequency hopping parameters, fitthe parameter model to the sequence set of samples by iterating over thefollowing: randomly selecting a first plurality of the samples from thesequence set of samples, estimating the plurality of first frequencyhopping parameters based on the selected first plurality of the samples,constructing a first instance of the parameter model based on theestimated plurality of first frequency hopping parameters, andclassifying the first plurality of the samples as inliers or outliers byevaluating fitting errors between the first plurality of the samples andthe constructed first instance of the parameter model, selecting one ofthe first instances of the parameter model based on the inliers andoutliers for each of the constructed first instances of the parametermodel, and identifying the first drone based on the selected firstinstance of the parameter model.

In another aspect, there is provided a method for detecting presence ofa drone, the method comprising: receiving a set of samples for a timeinterval, the set of samples comprising samples of a first RF signaltransmitted between a first drone and a first drone controller, thefirst RF signal including a first frequency hopping sequence defined bya plurality of first frequency hopping parameters; obtaining a parametermodel of the first frequency hopping parameters; fitting the parametermodel to the set of samples by iterating over the following: randomlyselecting a first plurality of the samples from the set of samples,estimating the plurality of first frequency hopping parameters based onthe selected first plurality of the samples, constructing a firstinstance of the parameter model based on the estimated plurality offirst frequency hopping parameters, and classifying the first pluralityof the samples as inliers or outliers by evaluating fitting errorsbetween the first plurality of the samples and the constructed firstinstance of the parameter model; selecting one of the first instances ofthe parameter model based on the inliers and outliers for each of theconstructed first instances of the parameter model; and identifying thefirst drone based on the selected first instance of the parameter model.

In yet another aspect, there is provided a non-transitory computerreadable storage medium having stored thereon instructions that, whenexecuted, cause a computing device to: receive receiving a set ofsamples for a time interval, the set of samples comprising samples of afirst RF signal transmitted between a first drone and a first dronecontroller, the first RF signal including a first frequency hoppingsequence defined by a plurality of first frequency hopping parameters;obtain a parameter model of the first frequency hopping parameters; fitthe parameter model to the set of samples by iterating over thefollowing: randomly selecting a first plurality of the samples from theset of samples, estimating the plurality of first frequency hoppingparameters based on the selected first plurality of the samples,constructing a first instance of the parameter model based on theestimated plurality of first frequency hopping parameters, andclassifying the first plurality of the samples as inliers or outliers byevaluating fitting errors between the first plurality of the samples andthe constructed first instance of the parameter model; select one of thefirst instances of the parameter model based on the inliers and outliersfor each of the constructed first instances of the parameter model; andidentify the first drone based on the selected first instance of theparameter model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example environment including a drone detectionsystem in accordance with aspects of this disclosure.

FIG. 2A illustrates an example drone detection system from FIG. 1 whichcan be used to detect, monitor, and/or mitigate drones in accordancewith aspects of this disclosure.

FIG. 2B illustrates an example drone from FIG. 1 which can be detectedwith the drone detection system of FIG. 2A in accordance with aspects ofthis disclosure.

FIG. 2C illustrates an example controller from FIG. 1 which can be usedto control the drone in accordance with aspects of this disclosure.

FIG. 2D illustrates another example drone detection system which can beused to detect the presence of one or more drones in accordance withaspects of this disclosure.

FIG. 2E illustrates an example of frequency hopping sequences used bytwo different drones in accordance with aspects of this disclosure.

FIG. 3 illustrates a method for detecting the presence of the drone inaccordance with aspects of this disclosure.

FIG. 4 illustrates a method for estimating the parameters of theparameter model in accordance with aspects of this disclosure.

FIG. 5 is an example method which can be performed by the dronedetection system to monitor the one or more drones.

FIG. 6 is an example method which can be performed by the dronedetection system to mitigate the one or more drones.

FIG. 7 is a graph showing the detection probability for differentnumbers of input samples for HopSAC and LS estimators with no grosserrors or timing errors.

FIG. 8 is a graph showing the detection probability under the impact ofgross errors.

FIG. 9 shows the detection probability of the embodiment of thetechniques disclosed herein and Least Squares (LS) estimators under theinfluence of timing errors in increasing variances of timing errors.

FIG. 10 shows the target detection probability for different numbers oftargets appearing in the input samples.

DETAILED DESCRIPTION

The fast growth of drone applications in industrial, commercial andconsumer domains in recent years has caused great security, safety andprivacy concerns. For this reason, demand has been growing for systemsand technique for drone detection, monitoring, and mitigation.

CUAS systems (or simply “drone detection systems”) may operate usingmultiple stages. In a first stage, the drone detection system detectsthe presence of a drone and determine whether the drone is a friend or afoe. The drone detection system can accomplish this by eavesdropping ormonitoring the signals exchanged between the drone and the controller.Certain drones can be configured to wirelessly communicate with acontroller using a frequency hopping protocol. Certain aspects of thisdisclosure may relate to detecting frequency hopping parameters whichcan be used to model frequency hopping-based communications between thedrone and the controller.

Small drones, which are widely used in recreational and commercialapplications, have caused alarming concerns of public safety andhomeland security due to frequently reported unauthorized droneincidents in recent years. To effectively disable potential threats fromfrequency hopping drones and controllers, drone detection systems may beconfigured to estimate the parameters of the frequency hopping signalswith high precision and low complexity for real-time responses.

Therefore, it is desirable to provide a model parameter estimationmethod to meet all these requirements for drone detection systems.Aspects of this disclosure relate to a hopping parameter estimationmethod based on random sample consensus (which can be referred to as“HopSAC”) to address the above described challenges. Advantageously,aspects of this disclosure can accurately estimate frequency hoppingparameters using a relatively small set of samples (in some instanceswith as few as two samples) in real-time. In one example, the methodsdisclosed herein can estimate the parameters of a linear frequencyhopping sequence and achieve high multiple target detection performancewith low implementation complexity that can be realized in real time.Simulation results show that the estimation techniques described hereinsignificantly outperform linear Least Squares methods in achievingexceptional accuracy of model parameter estimation under the impact ofgross errors, timing errors, and/or multiple drone targets.

FIG. 1 illustrates an example environment 100 including a dronedetection system 101 in accordance with aspects of this disclosure. Incertain embodiments, the environment 100 includes the drone detectionsystem 101, one or more drones 103A-103N, and one or more dronecontrollers 105A-105N (or simply “controllers”). An example of the oneor more drones 103A-103N is illustrated in FIG. 2B. An example of theone or more controllers 105A-105N is illustrated in FIG. 2C.

In certain embodiments, each of the drones 103A-103N is configured tocommunicate to a corresponding one of the controllers 105A-105N via anRF signal 107A-107N. Although not illustrated, in some embodiments, asingle one of the controllers 105A-105N may be configured to controlmore than one of the drones 103A-103N.

The drone detection system 101 is configured to receive monitor109A-109N the communications between the drones 103A-103N and thecontrollers 105A-105N in order to detect the presence of the drones103A-103N. For example, the drone detection system 101 may be configuredto receive the RF signals 107A-107N being sent between the drones103A-103N and the controllers 105A-105N in order to monitor 109A-109Nthe communication between the drones 103A-103N and the controllers105A-105N. In certain embodiments, once the drone detection system 101is able to decode the RF signals 107A-107N, the drone detection system101 may monitor the drones 103A-103N and take certain actions in orderto mitigate the potential threat of the drones 103A-103N. For example,as is explained with respect to FIG. 6, the drone detection system 101may transmit a jamming RF signal to disrupt communication between thedetected drone 103A-103N and the controller 105A-105N, and/or spoof thecontroller 105A-105N by sending a command to the drone 103A-103N to landor otherwise leave the environment 100.

FIG. 2A illustrates an example drone detection system 101 which can beused to detect the presence of the one or more drones 103A-103N inaccordance with aspects of this disclosure. In certain embodiments, thedrone detection system 101 includes a processor 111, a memory 113, afront end 115, a plurality of transmit antennae 117A-117N, and aplurality of receive antennae 119A-119N. In other embodiments, one ormore of the antennae 117A-119N can be used for both transmitting andreceiving signals.

In certain embodiments, the drone detection system 101 is configured toreceive an RF signal (e.g., the RF signals 107A-107N of FIG. 1) via oneof the receive antennae 119A-119N. The one of the receive antennae119A-119N provides the received RF signal to the front end 115. Incertain embodiments, the front end 115 can process the received RFsignal into a format that can be read by the processor 111. For example,in certain embodiments, the front end 115 may perform one or more of thefollowing actions: filtering, amplifying, analog-to-digital conversion,etc. on the received RF signal.

In certain embodiments, the memory 113 can store computer readableinstructions for causing the processor 111 to detect the presence of adrone (e.g., the drones 103A-103N of FIG. 1) based on the RF signalsreceived via the receive antennae 119A-119N. In addition, in certainembodiments, the drone detection system 101 can also be configured toprovide a signal (e.g., a jamming signal or an RF communication signal)to the front end 115 to be transmitted to the detected drone(s). Thefront end 115 can then process the signal received from the processor111 before providing the processed signal to one or more of the transmitantennae 117A-117N.

There are a number of different techniques that the drone detectionsystem 101 can use to detect the presence of the drones 103A-103N. Forexample, the drone detection system 101 can scan the airwaves atfrequencies known to be used by particular model(s) of the drones103A-103N. If a known protocol is identified, the drone detection system101 can then decode the signal as if it was the intendedreceiver/controller 105A-105N. Depending on the embodiment, thesedecoding steps can include: synchronization, channel estimation,deinterleaving, descrambling, demodulation, and error control decoding.In certain embodiments, the drone detection system 101 can be configuredto perform some of the aforementioned steps blindly due to lack ofknowledge (such as device id) on information known by the controller105A-105N. As described below, the blind detection of the drones103A-103N using certain communication protocols (e.g., a synchronizationsignal) are provided herein. Once detected, the drone detection system101 can provide alert(s) regarding the presence of the one or moredrones 103A-103N.

The drone detection system 101 can monitor the presence of the one ormore drones 103A-103N. As part of monitoring, a position of the one ormore drones 103A-103N relative to the environment 100 can be monitoredin real-time to determine if the position of the one or more drones103A-103N strays inside or outside acceptable airspace.

There are also a number of mitigation actions which can be taken by thedrone detection system 101. For example, after detecting the one or moredrones 103A-103N, the drone detection system 101 may take one or more ofthe actions described with reference to FIG. 6. For example, in certainembodiments, these actions can include do nothing/keep monitoring,drone-specific jamming, wideband jamming, and control takeover.

FIG. 2B illustrates an example drone 103 which can be detected with thedrone detection system 101 in accordance with aspects of thisdisclosure. In certain embodiments, the drone 103 includes one or morepropellers 121, one or more motor controllers 123, a battery or otherpower source 125, a memory 127, a processor 129, a front end 131, anantenna 133, and a camera 135. As described above, the antenna 133 maybe configured to receive RF signals 107 from the controller 105 (seeFIG. 2C) and provide RF signals 107 back to the controller 105 (e.g.,images obtained from the camera 135). In certain embodiments, the RFsignals 107 sent/received from the antenna 133 are provided to/from theprocessor 129 and processed by the front end 131. In certainembodiments, the propeller(s) 121 provide lift and control movement ofthe drone 103 as it maneuvers through airspace. The propeller(s) 121 mayalso include one or more motor(s) (not illustrate) configured toindividually power each of the propeller(s) 121.

In certain embodiments, the motor controller(s) 123 are configured toreceive instructions from the processor 129 (e.g., based on instructionsstored in the memory 127 and the RF signal 107 received from thecontroller 105) to move the drone 103 to a specific point in theairspace and translate the received instructions into motor positioncommands which are provided to the propeller(s) 121. In certainembodiments, the battery 125 provides power to each of the components ofthe drone 103 and has sufficient power storage to enable the propellers121 to maneuver the drone 103 for a predetermined length of time. Thecamera 135 can capture images in real-time and provide the capturedimages to the controller 105 via the antenna 133 which can aid a user incontrolling movement of the drone 103.

FIG. 2C illustrates an example controller 105 which can be used tocontrol the drone 103 in accordance with aspects of this disclosure. Incertain embodiments, the controller 105 comprises a memory 141, aprocessor 143, a front end 145, an antenna 147, an input device 149, anda display 151. As described above, the antenna 147 may be configured toreceive RF signals 107 (e.g., images obtained from the camera 135) fromthe drone 103 (see FIG. 2B) and provide RF signals 107 back to the drone103 to control movement of the drone 103. In certain embodiments, the RFsignals 107 sent/received from the antenna 147 are provided to/from theprocessor 143 and processed by the front end 145. In certainembodiments, the input device 149 is configured to receive input from auser which can be used by the processor 143 to generate commands forcontrolling movement of the drone 103. In certain embodiments, thedisplay 151 is configured to display images received from the drone 103to the user to provide feedback on the current position of the drone 103and its environment 100. In some embodiments, the display can beimplemented as a pair of goggles worn by the user to provide a firstperson view of images obtained by the camera 135.

FIG. 2D illustrates another example drone detection system 101 which canbe used to detect the presence of the one or more drones 103 inaccordance with aspects of this disclosure. In particular, the dronedetection system 101 illustrated in FIG. 2D is a simplified systemmodel. Depending on the implementation, the drone detection system 101can include a greater or fewer number of components than shown in FIG.2D. The drone detection system 101 includes a receive antenna 119, areceiver 161, a sensor/detector 163, a HopSAC estimator 165, a jammer167, a transmitter 169, and a transmit antenna 117. Although illustratedin separate blocks in FIG. 2D, one or more of the blocks 161-169 may beimplemented together by the same component(s). For example, in oneimplementation the receiver 161, the sensor/detector 163, the jammer167, and the transmitter 169 can be implemented by the front end 115illustrated in FIG. 2A, while the HopSAC estimator 165 can beimplemented by the processor 111 illustrated in FIG. 2A.

In certain embodiments, the drone detection system 101 can receivewideband frequency-hopping signals from all of the drones 103 and/orcontrollers 105 within a detection range of the drone detection system101. In certain embodiments, the drone detection system 101 can thenstore signal parameters for each of the received frequency-hoppingsignal in the form of pulse descriptor words (PDW) after preprocessing.In some implementations, preprocessing may involve generating clustersof the frequency-hopping signals based on one or more of the following:emitter type or angle of arrival, level control with power squelch,and/or correlation by matched filtering.

In certain embodiments, the sensor/detector 163 can determine thepresence of a given one of the frequency-hopping signals at time {tildeover (t)}_(n) and frequency {tilde over (f)}_(n), where n is the indexof the time-frequency samples. In certain embodiments, thesensor/detector 163 can send a set of time-frequency samples {({tildeover (t)}_(n), {tilde over (f)}_(n))} every T_(s) seconds to the HopSACestimator 165 for parameter estimation. In certain embodiments, theHopSAC estimator 165 can estimate the frequency-hopping parameters andprovide the estimated parameters to configure the jammer 167. In certainembodiments, the transmitter 169 can transmit interference signals forcounterattack via the transmit antenna 117.

Detection of Drone(s) Using Frequency Hopping

In recent years, the use of drones 103 has gained popularity due totheir affordability and versatility. Drones 103 have been widely used inmany applications from recreational flying such as drone racing tocommercial uses such as package delivery and real estate photography.According to FAA Forecast, the use of non-model (commercial) drones 103will grow three-fold from 2018 to 2023 whereas the use of model(recreational) drones 103 will increase from 1.25 to 1.39 million unitsin 5 years. However, unauthorized drone activities and incidents havebeen reported more and more frequently near airports, stadiums, andborders, which has caused growing concerns about public safety andhomeland security. Therefore, an effective drone detection system 101becomes an indispensable mechanism for law enforcement and military todetect, identify, and disable any potential and imminent threats causedby the improper and unauthorized uses of drones 103.

In certain embodiments, the drone detection system 101 is capable ofdetecting any wireless signal transmitted from the drones 103 and thecontrollers 105 when they are in the detection range, and determiningwhen in time and where in frequency the received signals are detected.Nevertheless, the time and frequency information are subject to errors,which can be caused by sensing and measurement errors, channelimpairments such as fading and interference, and/or hardware limitationssuch as carrier frequency offsets and timing jitters. One of the mainchallenges in detecting the drones 103 is to identify the frequencyhopping parameters from these noisy time-frequency samples. Theestimation of these parameters is further complicated by multi-targetscenarios where multiple drones 103 are present within the detectionrange.

There are many frequency hopping parameter estimation methods based on avariety of techniques: dynamic programming and low-rank trilineardecomposition, sparse linear regression, sparse Bayesian method, andorthogonal matching pursuit. The implementation complexity of thesemethods is generally too high to be performed in real-time. Thus, theyare not suitable for real-time applications that have critical responsetime constraints.

Aspects of this disclosure relates to systems and methods for detectionone or more drones 103 using a frequency hopping parameter estimationmethod (which may be referred to as “HopSAC”) based on the random sampleconsensus algorithm. Random sample consensus (RANSAC) is a technique forestimating parameters of a model by fitting the model to observationdata. Similar to RANSAC, certain methods described herein outperform thelinear Least Squares (LS) method for its excellent capability ofrejecting gross errors with only a small subset of samples. Grosserrors, also known as outliers, are data samples significantly deviatingfrom the majority of observations. The ability of the methods describedherein to discard gross errors allows the drone detection system 101 totolerate a higher false alarm rate for detection at a lowersignal-to-noise ratio (SNR). In addition, certain methods describedherein are very robust to timing and frequency errors, enabling thejammer 167 to transmit much more precise pulses in time, and thusimproving power efficiency and reducing collateral damage.

Aspects of the HopSAC techniques described herein provide effective waysfor drone detection systems 101 to achieve high performance ofestimation accuracy with low complexity in the real-time parameterestimation of frequency hopping drones and controllers.

Model of Frequency Hopping Communications

In order to describe the systems and techniques used for detection offrequency hopping parameters, the drone detection system 101 uses one ormore models of the linear hopping sequences used by the drones 103 forcommunication. That is, the drone 103 communicate with the controller105 using RF communications that hop between different frequencies (orchannels) during communication. As previously discussed, there may bemore than one drone 103 present within the detection range of the dronedetection system 101.

FIG. 2E illustrates an example of frequency hopping sequences used bytwo different drones 103 in accordance with aspects of this disclosure.In FIG. 2E, the red points illustrate a first frequency hopping sequence181 and the blue points illustrate a second frequency hopping sequence183. As shown in FIG. 2E, the first and second frequency hoppingsequences 181 and 183 each hop between a plurality of frequencies atregular time periods.

In certain embodiments, the frequency hopping sequences may be linearfrequency hopping sequences, such as the first and second frequencyhopping sequences 181 and 183 illustrated in FIG. 2E. Techniques fordetecting the drones 103 using linear frequency hopping sequences areprovided in detail herein, however, aspects of this disclosure can alsobe applied to other types of frequency hopping sequences, such as:pseudo random frequency hopping sequences (e.g., linear feedback shiftregister (LFSR)-based pseudo random hopping sequences), non-linearfrequency hopping sequences, arbitrary linear frequency hoppingsequences, etc.

A model of the linear frequency hopping sequences illustrated in FIG. 2Ecan be described using x={x_(n)} representing a sequence of 2-tuples andx_(n)=({tilde over (t)}_(n), {tilde over (f)}_(n)) representing thetime-frequency observations of frequency-hopping signals. Defining t_(n)and f_(n) as the time and the frequency, respectively, of drone 103transmissions, the timing errors can be defined as t_(δ,n)={tilde over(t)}_(n)−t_(n) and frequency errors f_(δ,n)={tilde over (f)}_(n)−f_(n).The timing and frequency errors t_(δ,n) and f_(δ,n) can be assumed to beindependent and identically distributed (i.i.d.) zero mean Gaussianvariables with variance σ_(t) ² and σ_(f) ², respectively. That is,t_(δ,n)˜N(0,σ_(t) ²) and f_(δ,n)˜N(0,σ_(f) ²). In addition tomeasurement errors, the occurrence of gross errors can be assumed tofollow a Poisson distribution with parameter λ, where λ is the averagenumber of gross errors per time interval T_(s) which can be referred toas an outlier arrival rate. Therefore, each sample x_(n) could be thetransmission of a real target drone or a false alarm caused by aspurious source or hardware impairments. It can be important todistinguish between the transmission of the real target drone 103 from afalse alarm in order to effectively detect the presence of the drones103 using frequency hopping.

As describe above, the techniques described herein can be applied toarbitrary frequency hopping models. However, certain linear hoppingsequences such as linear feedback shift register (LFSR)-based pseudorandom hopping sequences require a large number of samples for accuratefit due to a large number of hopping states. As an example, a linearhopping sequence f_(m) can be modeled as:f _(m)=(f ₀ −f _(b) +m·f _(d) mod B)+f _(b) , m=0, 1, . . .   (1)

where f₀ is the start frequency of the first hop after timestamp 0,f_(b) is the center frequency of the lowest channel, f_(d) is frequencydifference or frequency spacing between two neighboring hops, B=N_(c)·bis the total bandwidth of all channels, N_(c) is the number of channelsused in the hopping sequence, and b is the channel bandwidth, which maybe known to the drone detection system 101 in certain embodiments. Insome circumstances, not all hops may be observed and detected by thedrone detection system 101. If the channel center frequencies are mappedto channel numbers starting from channel 0, (1), the linear frequencyhopping sequence f_(m) can be simplified to:c _(m) =c ₀ +m·c _(d) mod N _(c) , m=0, 1, . . .   (2)

where c_(m)=(f_(m)−f_(b))/b, c₀=(f₀−f_(b))/b, and c_(d)=f_(d)/b. c_(d)can also be referred to as hop number or hop count. The correspondingtimestamp t_(m) of each hop can be given by:t _(m) =t ₀ +m·t _(d) , m=0, 1, . . .   (3)

where t₀ is the start time of the first hop and t_(d) is the timeinterval between two neighboring hops known as hop period. Hence, insome embodiments, the frequency hopping parameter model contains fiveparameters: i) time start t₀, ii) frequency start f₀ or channel startc₀, iii) hop period t_(d), iv) frequency spacing f_(d) or hop countc_(d), and v) number of channels N_(c). In the example of a ofLFSR-based pseudo random sequence, the parameters can include thesequence length N (e.g., the number of LFSR stages L) and thepolynomials (g₀, . . . , g_(L−1)), given the mapping from binarysequences to center frequencies of channels.

The operating frequencies of small model drones may be publicly knownand published in FCC test reports. For example, 39 channels may bedefined from 2404 MHz to 2480 MHz in 2.4 GHz band for DJI GL300A remotecontroller. The differences of operating frequencies can be derived fromall possible center frequency of channels. FIG. 2E shows an example offrequency hopping sequences from DJI remote controllers GL300A andGL300F. The first frequency hopping sequence 181 can be defined by theparameters: f_(b)=2404 MHz, t₀=0.012 s, f₀=2456.7 MHz, t_(d)=0.014 s,f_(d)=32.3 MHz, N_(c)=45, and b=1.7 MHz, and the second frequencyhopping sequence 183 can be defined by the parameters: f_(b)=2404 MHz,t₀=0.007 s, f₀=2414 MHz, t_(d)=0.014 s, f_(d)=10 MHz, N_(c)=39, and b=2MHz.

Overview of Techniques for Detecting Frequency Hopping Parameters

FIG. 3 illustrates a method 300 for detecting the presence of the drone103A-103N in accordance with aspects of this disclosure. Specifically,the method 300 involve detecting frequency hopping parameters for theone or more drones 103 within a detection range. In particular, thedrone detection system 101 can detect the parameters that define thefrequency hopping sequence used for communication between the one ormore drones 103A-103N and the controllers 105A-105N in order to detectthe presence of the one or more drones 103A-103N.

The method 300 begins at block 301. At block 302, the method 300involves receiving a set of samples from the RF receiver for a timeinterval. In some implementations, the method 300 can involve receivinga vector of time-frequency samples x of length ∥x∥=N_(s) from thesensor/detector 163 for each time interval T_(s).

At block 304, the method 300 involves obtaining a parameter model of thefrequency hopping parameters. For example, when detecting the one ormore drones 103 using a linear frequency hopping sequence, the parametermodel may correspond to one of the models defined by equations (1) and(2). However, aspects of this disclosure are not limited to theparameter models described in equations (1) and (2), and other parametermodels can be used in other embodiments.

At block 306, the method 300 involves fitting the parameter model to thesamples. Aspects of this disclosure relate to using the RANSAC paradigmin fitting the parameter model to the samples. In some implementations,the fitting of the parameter model to the samples involves randomlyselecting n_(s) samples from the set of samples x, using the selectedsamples n_(s) to estimate the parameters, constructing an instance ofthe parameter model using the estimated parameters, and classifying thesamples n_(s) as inliers or outliers by evaluating fitting errors foreach of the samples n_(s). Here inliers refer to the samples closelymatching the parameter model with negligible or no fitting errors (e.g.,with a fitting error less than a threshold error value) whereas outliersare samples resulting in large errors (gross errors) from the fitting(e.g., with a fitting error equal to or greater than the threshold errorvalue).

The block 306 process of fitting the parameter model to the samples maybe repeated for N_(r) trials to construct a plurality of instances ofthe parameter model. At block 308, the method 300 involves selecting oneof the first instances of the parameter model based on the inliers andoutliers for each of the constructed first instances of the parametermodel. In certain embodiments, for example, the “best” model, M, can beselected as the instance of the parameter model having the largestnumber of inliers |I| as the largest group of samples, I, reaches theconsensus.

At block 310, the method 300 can involve identifying a first targetdrone 103 based on the selected instance of the parameter model. Afterselecting the instance of the parameter model for the first target drone103, the instance of the parameter model can be stored in a target setT. The method 300 can further involve subtracting the set of inliersfrom the set of samples because these inliers already formed theconsensus group associated with the first target drone 103. As a result,the set of outliers, O, can be used as input samples x to find a bestmodel of a second target drone 103. Thus, the method 300 can be repeatedstarting from block 302 with the received set of samples being thesubset of samples with the inliers from the first target drone 103 beingremoved. The method 300 can be repeated until no new target drone 103 isfound or the number of outliers remaining in the set of samples isinsufficient for performing the method 300. Once the method 300 iscompleted, the target set T contains the instances of the parametermodel for all of the detected target drones 103.

Estimation of Model Parameters

FIG. 4 illustrates a method 400 for estimating the parameters of theparameter model in accordance with aspects of this disclosure. Forexample, the method 400 can be performed as a part of block 306 ofmethod 300 for fitting the parameter model to the selected samples.

In certain embodiments, the model parameter estimation procedureestimates the model parameters using the randomly selected samples andthe knowledge of define channel center frequencies F_(i). The dronedetection device can store a set F=∪_(i) F_(i) of publicly known anddefined channel center frequencies F_(i) for frequency hopping devicetype i. The frequency differences of any two defined channel frequenciesand the total number of channels for a given device type i may also beknown based on the prior knowledge of F_(i).

With reference to FIGS. 3 and 4, in each trial r of the method 300, themethod 400 can be used to estimate the model parameters of each devicetype i using the prior knowledge of the channel center frequenciesF_(i), and obtain a parameter model M′ of this device type. In certainembodiments, by using the model parameters in the device type parametermodel M′, the drone detection system 101 can calculate the fittingerrors of all samples from the set of samples and classify each sampleas an inlier or an outlier depending on the fitting errors between theparticular sample and the device type parameter model M′.

The device type parameter model M′ can be compared to the instance ofthe parameter model M selected in block 308 if the selected instance ofthe parameter model M was found in previous trials r. In certainembodiments, the selected instance of the parameter model M can bereplaced with device type parameter model M′ if the number of inliers,|I′|, of the device type parameter model M′ is greater than the numberof inliers of the selected instance of the parameter model M. That is,the device type parameter model M′ may be more likely to be the bestestimate of the target drone 103 when a larger number of inliersreaching the consensus are associated with the device type parametermodel M′ than the selected instance of the parameter model M. At the endof all trials r, the best model remaining in selected instance of theparameter model M will be the model of the detected target drone 103with a specific set of estimated hopping parameters.

In certain embodiments, the model parameter estimation procedureestimates model parameters using randomly selected samples and theknowledge of the defined channel center frequencies F_(i). Withreference to FIG. 4, at block 402 the method 400 involves randomlyselecting n_(s) samples from the observations in time interval T_(s).For a linear hopping model such as the model defined in equations (1)and (2), as few as two samples (n_(s)=2) randomly selected each time maybe sufficient for parameter estimation. In certain embodiments, n_(s)samples are first randomly selected: ({tilde over (t)}_(j), {tilde over(f)}_(j)), j=1, 2, . . . , n_(s). The sets of time differences andfrequency differences between any of two samples can be calculated as:D_(t)={D_(t,j,k)∥{tilde over (t)}_(j)−{tilde over (t)}_(k)|, j<k} andD_(f)={D_(f,j,k)∥{tilde over (f)}_(j)−{tilde over (f)}_(k)|, {tilde over(t)}_(j)<{tilde over (t)}_(k), j<k}, respectively. There are

$n_{c} = \begin{pmatrix}n_{s} \\2\end{pmatrix}$elements in D_(t) and in D_(f).

At block 404, the method 400 involves estimating the frequency hoppingparameters. For example, in one example, block 404 can involve firstfinding new estimates of the hop period t_(d) and the start time of thefirst hop t₀. The sensor/detector 163 can provide a coarse estimate{tilde over (t)}_(d) of the hop period t_(d). For each d_(t,j,k)∈D_(t),the number of hop intervals between samples ({tilde over (t)}_(j),{tilde over (f)}_(j)) and ({tilde over (t)}_(k), {tilde over (f)}_(k))can be found by:n _(h,j,k) =└d _(i,j,k) /{tilde over (t)} _(d)┐  (4)

where └⋅┐ denotes a rounding function. The hop period t_(d) estimate foreach d_(t,j,k) can be obtained as:{circumflex over (t)} _(d,j,k) =d _(i,j,k) /n _(h,j,k).  (5)

The start time of the first hop t₀ can then be estimated as:{circumflex over (t)} _(0,j,k)=((s _(f) ·t _(j))mod(s _(f) ·{circumflexover (t)} _(d,j,k)))/s _(f)  (6)

where s_(f) is the stability factor for numerical stability.

The drone detection system 101 can next find the new estimates of thefrequency start f₀ and the frequency spacing f_(d). From the channelcenter frequencies F_(i), the set of differences of operatingfrequencies in the channel center frequencies F_(i) isD_(i)={|f_(j)−f_(k)|, j≠k, f_(j), f_(k), ∈F_(i)}. For eachd_(f,j,k)∈D_(f), if d_(f,j,k) matches any elements in D_(i) within asmall error δ_(f), the estimate of frequency difference between hops,{circumflex over (f)}_(d,j,k), is the f_(d,j,k) that satisfies:f _(k)=((f _(j) −f _(b) +n _(h,j,k) ·f _(d,j,k))mod B)+f _(b).  (7)

To find the start frequency f_(0,j,k), the method 400 can involve firstfinding the number of hops from ({tilde over (t)}_(j), {tilde over(f)}_(j)) to the first sample, n_(0,j,k), given by:n _(0,j,k)=└({tilde over (t)} _(j) −{circumflex over (t)}_(0,j,k))/{circumflex over (t)} _(d,j,k)┐  (8)

The start frequency f_(0,j,k) can be estimated as:{circumflex over (f)} _(0,j,k)=((f _(j) −f _(b) −n _(0,j,k) ·{circumflexover (f)} _(d,j,k))mod B)+f _(b).  (9)

If the estimates in the same group are the same or only differ in anegligible value, they can be determined to be valid estimates and aninstance of the parameter model can be created using the validestimates. That is:{circumflex over (t)} ₀=Σ_(j,k) {circumflex over (t)} _(0,j,k) /n _(c)if |{circumflex over (t)} _(0,j,k) −{circumflex over (t)} _(0,m,n)|<δ_(t)  (10){circumflex over (t)} _(d)=Σ_(j,k) {circumflex over (t)} _(d,j,k) /n_(c) if |{circumflex over (t)} _(d,j,k) −{circumflex over (t)}_(d,m,n)|<δ _(t)  (11){circumflex over (f)} ₀=Σ_(j,k) {circumflex over (f)} _(0,j,k) /n _(c)if |{circumflex over (f)} _(0,j,k) −{circumflex over (f)} _(0,m,n)|<δ_(f)  (12){circumflex over (f)} _(d)=Σ_(j,k) {circumflex over (f)} _(d,j,k) /n_(c) if |{circumflex over (f)} _(d,j,k) −{circumflex over (f)}_(d,m,n)|<δ _(f)  (13)

j≠m, k≠n∀j, k, n, m. In addition, {tilde over (t)}₀={tilde over(t)}_(0,j,k), {tilde over (t)}_(d)={tilde over (t)}_(d,j,k), {tilde over(f)}₀{tilde over (f)}_(0,j,k), {tilde over (f)}_(d)={tilde over(f)}_(d,j,k) if n_(c)=1 (n_(s)=2). The number of channels N_(c) may besimply |F_(i)|. In block 406, the method 400 can construct an instanceof the parameter model as M′={{circumflex over (t)}_(d), {circumflexover (t)}₀, {circumflex over (f)}_(d), {circumflex over (f)}₀,{circumflex over (N)}_(c),}. As described above, at block 408, themethod 400 can involve classifying the samples n_(s) as inliers oroutliers by evaluating fitting errors for each of the samples n_(s). Forexample, the drone detection system 101 can compare the fitting error ofeach of the samples n_(s) to the threshold error value and classify thesamples n_(s) that have a fitting error that is less than the thresholderror value as inliers and the remaining samples n_(s) as outliers. Themethod 400 ends at block 410.

Classification of Inliers and Outliers

An embodiment of the classification process of block 408 can begin witha fitting error evaluation, which involves calculates the fitting errorsof all samples based on the current instance of the parameter model inM′. In some implementations, for each sample k, the drone detectionsystem 101 can find a time fitting error ϵ_(t,k) and a frequency fittingerror ϵ_(f,k) separately. The time fitting error ϵ_(t,k) can becalculated with the assistance of s_(f) for numerical stability as:ϵ_(t,k)=(└{tilde over (t)} _(k) ·s _(f)┐ mod └{circumflex over (t)} _(d)·s _(f)┐)/(s _(f) −{circumflex over (t)} ₀).  (14)

To find frequency fitting error ϵ_(f,k), the drone detection system 101can first determine hop phase h_(p), which is the number of hopintervals from the beginning of the set of samples to the current sample({tilde over (t)}_(k), {tilde over (f)}_(k)) given by:h _(p,k) =└{tilde over (t)} _(k) −{circumflex over (t)} ₀ ┐/{circumflexover (t)} _(d)  (15)

The drone detection system 101 can then estimate the frequency of thecurrent sample based on model parameters as:{circumflex over (f)} _(k)=(({circumflex over (f)} ₀ −f _(b) +h _(p,k)·{circumflex over (f)} _(d))mod B)+f _(b)  (16)

The frequency fitting error can then be determined asϵ_(f,k)={circumflex over (f)}_(k)−{tilde over (f)}_(k). The dronedetection system 101 can then determine the combined fitting error ϵ bythe square root of the sum of time error ϵ_(t,k) squared and frequencyerror ϵ_(f,k) squared. That is, ϵ_(k)=√{square root over (ϵ_(t,k)²+ϵ_(f,k) ²)}. The fitting error of each sample, ϵ_(k), can be comparedto a fitting error threshold γ. If ϵ_(k) is smaller than γ, sample x_(k)is an inlier and is included in the set of inliers, I′, otherwise, it isan outlier, which is included in the set of outliers, O′.

Example Method for Monitoring the Drone(s)

Once a drone 103 has been detected, for example, using one of the abovemetrics, the drone detection system 101 can monitor the drone 103 for aperiod of time before potentially taking mitigation actions against thedrone 103. FIG. 5 is an example method 500 which can be performed by thedrone detection system 101 to monitor one or more of the drones 103.

The method 500 begins at block 501. In certain implementations, themethod 500 may be performed in response to detecting the drone 103 basedon the method 300 of FIG. 3. At block 502, the method 500 involvesdecoding the RF signal 107 transmitted between the drone 103 and thecontroller 105. In the case where the frequency hopping parameters ofthe RF signal 107 are estimated in accordance with aspects of thisdisclosure, the decoding of the RF signal 107 may be performed byconfiguring the receiver 161 to follow the reconstructed frequencyhopping sequence using these parameters and synchronize with the RFsignal 107.

At block 504, the method 500 involves storing data associated with thedrone 103 from the decoded RF signal 107. For example, the data caninclude any activities performed by the drone 103 (e.g., flight data),drone behaviors that may indicate whether the drone 103 is a friend orfoe, a unique drone identifier, etc. At block 506, the method 500involves determining whether to perform mitigation actions on the drone103. In response to determining that mitigation actions are warranted,the method 500 may end at block 506, where a mitigation method 600 canbe performed. In response to determining that mitigation actions are notwarranted, the method 500 returns to block 502.

Example Method for Mitigating the Drone(s)

After determining that mitigation of the drone 103 is appropriate and incertain embodiments, the drone detection system 101 performs one or moreof a number of different mitigation actions in accordance with aspectsof this disclosure. FIG. 6 is an example method 600 which can beperformed by the drone detection system 101 to mitigate one or more ofthe drones 103.

The method 600 begins at block 601. In certain implementations, themethod 600 may be performed in response to determining that mitigationactions are warranted in block 506 of FIG. 5. At block 602, the method600 involves selecting a mitigation technique to perform. The method 600then involves continuing to one of blocks 604-610 based on themitigation technique selected in block 602.

At block 604, the method 600 involves continuing to monitor the drone103, which may involve returning to block 502 of method 500. Forexample, if the drone 103 is determined to be friendly and/or if thedrone detection system 101 does not have the legal authority to takemore aggressive actions, the drone detection system 101 may only beauthorized to continue monitoring the drone 103 while alerting a user tothe presence of the drone 103.

At block 606, the method 600 involves performing drone specific jamming.For example, in the case that the drone detection system 101 hasestimated the frequency hopping parameters used by the drone 103, thedrone detection system 101 can configured the jammer 167 with theestimated parameters. The jammer 167 can then generate a jamming signaland transmit the jamming signal to all drones 103 within the detectionrange via the transmit antenna 117 to disrupt the RF communicationsbetween the drones 103 and the controllers 105.

At block 608, the method 600 involves the drone detection system 101performing wideband jamming. In certain embodiments, wideband jammingmay be appropriate where the drone detection system 101 does not havesufficient knowledge of the communication protocol used by the RF signal107 to perform drone specific jamming and where the wideband jammingwill not affect other friendly drones 103 within the environment 100.

At block 610, the method involves the drone detection system 101 takingover control of the drone 103. For example, and in certain embodiments,using the estimated frequency hopping parameters estimated in accordancewith aspects of this disclosure to reconstruct the frequency hopping RFsignal 107, the drone detection system 101 can send commands to thedrone 103 in order to have the drone 103 perform certain maneuvers, suchas landing the drone 103 in a safe area. The method 600 ends at block612.

Simulations

The following description provides a verification of the abovetechniques for detecting the one or more drone 103A-103N. Theverification is performed for an embodiment of the techniques describedherein for hopping parameter estimation using Monte Carlo simulationwith N_(iter)=10,000 iterations. The performance is also compared to theperformance of linear LS estimation under the influence of gross errors,timing errors, and multiple targets. The parameter settings insimulations are tabulated in Table 1.

TABLE 1 Symbol Value Description T_(s) 0.5 s Sample acquiring timeinterval N_(s) 35 Number of input samples in T_(s) n_(s) 2 Number ofrandomly selected samples N_(t) 1-10 Number of targets {tilde over(t)}_(d) 14 ± 0.1 ms Coarse hop period δ _(t) 0.001 ms Hop periodthreshold δ _(f) 100 Hz Frequency difference threshold γ 0.01 Fittingerror threshold N_(r) 100 Number of trials N_(iter) 10,000 Number ofMonte Carlo iterations s_(f) 1000 Stability factor F_(i) — Channeldefinition of type i N_(c) — Number of channels

The following simulations are based on three linear frequency hoppingdevices (e.g., target drones 103). The spectrogram in 2.4 GHz band showsthat the coarse time period {tilde over (t)}_(d) between hops is roughly14 ms. In each iteration, the simulations generate N_(t) target dronesby randomly selecting N_(t) devices from these three devices underconsideration. The frequency hopping sequence of each target drone isconfigured by parameters t₀∈[0,0.014), t_(d)∈[0.01399, 0.0141],f₀∈F_(i), f_(d)∈F_(i), N_(c)=|F_(i)|, where F_(i) is determined by theselected device i. Note that the same device may be selected multipletimes as multiple targets with different hopping parameters and hoppingbehavior. In the simulations, the hopping samples from all targets arecombined, N₀ outliers are added where N₀ is determined by Poissonprocess with outlier arrival rate λ, and randomly placed in timeinterval T_(s) and in frequency range defined in F, and t_(k) isadjusted with timing error following Gaussian distribution N(0, σ_(t)²). Finally, all time-frequency samples are sorted in time domain toform the input samples.

The performance of the simulated embodiment and LS estimators isevaluated by using the metric: target detection probability P_(d)defined as:

$\begin{matrix}{P_{d} = {\frac{1}{N_{iter} \cdot N_{t}}{\sum\limits_{j = 1}^{N_{iter}}{\sum\limits_{k = 1}^{N_{t}}{\prod\limits_{\ell = 1}^{5}I_{j,k,\ell}}}}}} & (17)\end{matrix}$

where I_(j,k,l){x} is an indicator function of iteration j for target kand test l that returns 1 when x is true and 0 otherwise, I_(j,k,1)={|t₀^(k)−{circumflex over (t)}₀ ^(j)|<δ _(t)}, I_(j,k,2)={|t_(d)^(k)−{circumflex over (t)}_(d) ^(j)|<δ _(t)}, I_(j,k,3)={c₀ ^(k)≡ĉ₀^(j)}, I_(j,k,4)={c_(d) ^(k)≡ĉ_(d) ^(j)}, I_(j,k,5)={N_(c)^(k)≡{circumflex over (N)}_(c) ^(j)} are the five test criteria, t₀^(k), t_(d) ^(k), c₀ ^(k), c_(d) ^(k), and N_(c) ^(j) are true hoppingparameters of target k, and {circumflex over (t)}₀ ^(j), {circumflexover (t)}₀ ^(j), ĉ₀ ^(j), ĉ_(d) ^(j), and {circumflex over (N)}_(c) ^(j)are best hopping parameter estimates of iteration j. Initially, thenumber of input samples required for satisfying target detectionperformance are found. Then the performance of single target detection(N_(t)=1) under the impact of gross errors and timing errors areevaluated. Finally, the parameter estimation performance in multipletarget scenarios is evaluated.

Number of Input Time-Frequency Samples

Initially, in certain embodiments, the process determines the number ofsamples required from the detector in each sensing/acquiring periodT_(s) to achieve a certain level of model parameter estimation accuracyand target detection probability P_(d) found. FIG. 7 is a graph showingthe detection probability P_(d) for different numbers of input samples(N_(s)) for HopSAC and LS estimators with no gross errors (λ=0) ortiming errors (σ_(t) ²=0). As shown in FIG. 7, the embodiment of thetechniques described herein requires a slightly smaller number ofsamples to achieve P_(d)=0.9 than Least Squares (LS). To achieve closeto 100% accuracy, both methods require approximately 35 samples. Forfair comparison, the acquiring period was set to T_(s)=0.5 s for theremaining simulations unless stated otherwise, which generates about 35time-frequency samples for coarse hop period {tilde over (t)}_(d)=0.014s.

Impact of Gross Errors

Next, the impact of gross errors on the accuracy of model parameterestimation was evaluated. FIG. 8 is a graph showing the detectionprobability under the impact of gross errors. FIG. 8 shows the detectionprobability for the embodiment of the techniques described herein and LSestimators with different degrees of outlier arrival rates λ in theinput samples. For the same acquiring period (T_(s)=0.5 s), theperformance of the method according to aspects of this disclosure isvirtually unaffected by increasing outlier arrival rates whereas theperformance of LS degrades significantly and drops to less than 50% evenwith a single outlier. This result is expected because the disclosedtechniques, like RANSAC, are capable of rejecting gross errors. LS, onthe other hand, uses all samples including outliers for estimation. As aresult, any outlier of large gross errors can considerably influence theaccuracy of the fitting. In certain embodiments, since the targetdetection probabilities of the estimator in accordance with aspects ofthis disclosure are all above 0.996 for different levels of grosserrors, this excellent ability to discard gross errors means that theimpact of false positives is lower and a lower detection threshold canbe used (e.g. a lower Constant False Alarm Rate (CFAR) threshold).Therefore, the detector is able to achieve better detection probabilityat a lower SNR.

Impact of Timing Errors

The effect of timing errors on the performance of hopping parameterestimation is considered next. FIG. 9 shows the detection probability ofthe embodiment of the techniques disclosed herein and LS estimatorsunder the influence of timing errors in increasing variances of timingerrors. As shown in FIG. 9, the techniques described herein dramaticallyoutperform LS with the same number of samples. Unlike LS, aspects ofthis disclosure are very robust to small timing errors with varianceσ_(t) ²<0.01. Since it is unlikely to observe huge timing errors withfunctional hardware (e.g. timing jitters are typically on the order ofpicoseconds), aspects of this disclosure are very robust to timingerrors caused by timing jitters. The vastly improved robustness totiming errors also means that the pulses transmitted by the CUAS jammercan be much more precise in time. This improves the power efficiency ofCUAS transmitters, which reduces collateral damage and unwantedinterference to other legitimate UAS systems.

Multiple Target Scenarios

Finally, the performance of HopSAC and LS estimators for multiple targetdetection is evaluated. FIG. 10 shows the target detection probabilityfor different numbers of targets appearing in the input samples. Thenumber of input samples N_(s) increases proportionally with the numberof targets N_(t) in the same acquiring interval T_(s)=0.5 s. In FIG. 10,it can be seen that certain embodiments in accordance with aspects ofthis disclosure achieve target detection probability greater than 0.994for detecting up to 10 targets. The results confirm that HopSAC isconsistent and performs well with multiple target detection. Incontrast, LS is unable to detect anything in multiple target scenarioseven with only two targets. In certain embodiments, the capability oftechniques of this disclosure for detecting multiple targets lies in itsability to classify the outliers and inliers, associate each set ofinlier samples to a specific target one by one, and extract outliersfrom all samples for the estimation of other targets. For this reason,the complexity of aspects of this disclosure grows linearly with thenumber of targets for detection. On the contrary, the LS estimator usesall samples for estimation regardless of the number of targets. Thesamples from transmissions of one target are outliers of other targets.Thus, the LS estimator is greatly affected by multiple targettransmissions and is unable to accurately estimate the parameters in anymultiple target scenarios.

In this disclosure, HopSAC frequency hopping parameter estimationmethods and systems based on the RANSAC paradigm for drone detectionsystems 101 are disclosed. Aspects of this disclosure are shown tooutperforms the LS method, and achieve virtually 100% estimationaccuracy under the impact of gross errors, small timing errors, and thepresence of multiple targets. The exceptional gross error rejectioncapability of this disclosure allows the drone detection system 101 totolerate higher false positives with good detection performance in lowSNR regions. Moreover, the robustness of aspects of this disclosure totiming errors significantly reduces response time and collateral damagewhile increasing the precision of transmissions and power efficiency.Finally, aspects of this disclosure excel in multiple target detectionwith exceptionally high detection probability at the expense of linearlyincreasing complexity.

Implementing Systems and Terminology

Implementations disclosed herein provide systems, methods and apparatusfor detecting the presence of drones. It should be noted that the terms“couple,” “coupling,” “coupled” or other variations of the word coupleas used herein may indicate either an indirect connection or a directconnection. For example, if a first component is “coupled” to a secondcomponent, the first component may be either indirectly connected to thesecond component via another component or directly connected to thesecond component.

The drone detection functions described herein may be stored as one ormore instructions on a processor-readable or computer-readable medium.The term “computer-readable medium” refers to any available medium thatcan be accessed by a computer or processor. By way of example, and notlimitation, such a medium may comprise RAM, ROM, EEPROM, flash memory,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer. It should be noted that acomputer-readable medium may be tangible and non-transitory. As usedherein, the term “code” may refer to software, instructions, code ordata that is/are executable by a computing device or processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isrequired for proper operation of the method that is being described, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

As used herein, the term “plurality” denotes two or more. For example, aplurality of components indicates two or more components. The term“determining” encompasses a wide variety of actions and, therefore,“determining” can include calculating, computing, processing, deriving,investigating, looking up (e.g., looking up in a table, a database oranother data structure), ascertaining and the like. Also, “determining”can include receiving (e.g., receiving information), accessing (e.g.,accessing data in a memory) and the like. Also, “determining” caninclude resolving, selecting, choosing, establishing and the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

The previous description of the disclosed implementations is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these implementations will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other implementations without departingfrom the scope of the invention. For example, it will be appreciatedthat one of ordinary skill in the art will be able to employ a numbercorresponding alternative and equivalent structural details, such asequivalent ways of fastening, mounting, coupling, or engaging toolcomponents, equivalent mechanisms for producing particular actuationmotions, and equivalent mechanisms for delivering electrical energy.Thus, the present invention is not intended to be limited to theimplementations shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. A system for detecting presence of one or moredrones, the system comprising: a radio-frequency (RF) receiverconfigured to receive a first RF signal transmitted between a firstdrone and a first drone controller, the first RF signal including afirst frequency hopping sequence defined by a plurality of firstfrequency hopping parameters; a processor; and a non-transitorycomputer-readable memory in communication with the processor and havingstored thereon computer-executable instructions to cause the at leastone processor to: receive a set of samples from the RF receiver for atime interval, the set of samples comprising samples of the first RFsignal, obtain a parameter model that represents the first frequencyhopping sequence transmitted between the first drone and the first dronecontroller, the parameter model comprising a plurality of modelparameters respectively modelling the first frequency hoppingparameters, fit the parameter model to the set of samples by iteratingover the following: randomly selecting a first plurality of the samplesfrom the set of samples, estimating values of the plurality of modelparameters based on the selected first plurality of the samples,constructing a first instance of the parameter model, the first instanceof the parameter model comprising the estimated values of the pluralityof model parameters, and classifying the first plurality of the samplesas inliers or outliers by evaluating fitting errors between the firstplurality of the samples and the constructed first instance of theparameter model, selecting one of the first instances of the parametermodel based on the inliers and outliers for each of the constructedfirst instances of the parameter model, and identifying the first dronebased on the selected first instance of the parameter model.
 2. Thesystem of claim 1, wherein the RF receiver is further configured toreceive a second RF signal transmitted between a second drone and asecond drone controller, the second RF signal including a secondfrequency hopping sequence defined by a plurality of second frequencyhopping parameters, wherein the set of samples further comprises samplesof the second RF signal, and wherein the computer-readable memoryfurther has stored thereon computer-executable instructions to cause theat least one processor to: fit the parameter model to a subset of theset of samples corresponding to the outliers that do not fit theselected first instance of the parameter model by iterating over thefollowing: randomly selecting a second plurality of samples from thesubset, estimating the values of the plurality of model parameters basedon the selected second plurality of samples, constructing a secondinstance of the parameter model based on the estimated values of theplurality of model parameters, and classifying the second plurality ofthe samples as inliers or outliers by evaluating fitting errors betweenthe second plurality of the samples and the second instance of theparameter model, selecting one of the second instances of the parametermodel based on the inliers and outliers for each of the constructedsecond instances of the parameter model, and identifying the seconddrone based on the selected second instance of the parameter model. 3.The system of claim 1, wherein the estimating of the values of theplurality of model parameters is further based on a set of predefinedchannel center frequencies.
 4. The system of claim 3, wherein theplurality of first frequency hopping parameters comprise a start time ofa first hop, a hop period between two neighboring hops, a startfrequency of a first hop after an initial time, a frequency differencebetween the two neighboring hops, and a number of channels.
 5. Thesystem of claim 1, wherein the classifying of the first plurality of thesamples as inliers or outliers comprises: determine a time fitting errorand a frequency fitting error for each sample of the first plurality ofthe samples based on the constructed first instance of the parametermodel, determine a combined fitting error for each sample of the firstplurality of the samples based on the time fitting error and thefrequency fitting error of the corresponding sample, and classify eachsample of the first plurality of the samples as the inlier or theoutlier based on a comparison of the corresponding combined fittingerror to a fitting error threshold.
 6. The system of claim 1, whereinthe constructed first instance of the parameter model having a greatestnumber of inliers is selected.
 7. The system of claim 1, wherein theparameter model comprises a model of one of the following: a linearfrequency hopping sequence, a pseudo random frequency hopping sequences,a non-linear frequency hopping sequences, and an arbitrary linearfrequency hopping sequence.
 8. The system of claim 1, furthercomprising: a jammer configured to receive the estimated values of theplurality of model parameters and generate a jamming RF signal todisrupt communication between the first drone and the first dronecontroller based on the estimated values of the plurality of modelparameters.
 9. A method for detecting presence of a drone, the methodcomprising: receiving a set of samples for a time interval, the set ofsamples comprising samples of a first RF signal transmitted between afirst drone and a first drone controller, the first RF signal includinga first frequency hopping sequence defined by a plurality of firstfrequency hopping parameters; obtaining a parameter model thatrepresents the first frequency hopping sequence transmitted between thefirst drone and the first drone controller, the parameter modelcomprising a plurality of model parameters respectively modelling thefirst frequency hopping parameters; fitting the parameter model to theset of samples by iterating over the following: randomly selecting afirst plurality of the samples from the set of samples, estimatingvalues of the plurality of model parameters based on the selected firstplurality of the samples, constructing a first instance of the parametermodel, the first instance of the parameter model comprising theestimated values of the plurality of model parameters, and classifyingthe first plurality of the samples as inliers or outliers by evaluatingfitting errors between the first plurality of the samples and theconstructed first instance of the parameter model; selecting one of thefirst instances of the parameter model based on the inliers and outliersfor each of the constructed first instances of the parameter model; andidentifying the first drone based on the selected first instance of theparameter model.
 10. The method of claim 9, wherein the set of samplesfurther comprises samples of a second RF signal transmitted between asecond drone and a second drone controller, the second RF signalincluding a second frequency hopping sequence defined by a plurality ofsecond frequency hopping parameters, wherein the method furthercomprises: fitting the parameter model to a subset of the set of samplescorresponding to the outliers that do not fit the selected firstinstance of the parameter model by iterating over the following:randomly selecting a second plurality of samples from the subset,estimating the values of the plurality of model parameters based on theselected second plurality of samples, constructing a second instance ofthe parameter model based on the estimated values of the plurality ofmodel parameters, and classifying the second plurality of the samples asinliers or outliers by evaluating fitting errors between the secondplurality of the samples and the second instance of the parameter model;selecting one of the second instances of the parameter model based onthe inliers and outliers for each of the constructed second instances ofthe parameter model; and identifying the second drone based on theselected second instance of the parameter model.
 11. The method of claim9, wherein the estimating of the values of the plurality of firstfrequency hopping parameters is further based on a set of predefinedchannel center frequencies.
 12. The method of claim 11, wherein theplurality of first frequency hopping parameters comprise a start time ofa first hop, a hop period between two neighboring hops, a startfrequency of a first hop after an initial time, a frequency differencebetween the two neighboring hops, and a number of channels.
 13. Themethod of claim 9, wherein the classifying of the first plurality of thesamples as inliers or outliers comprises: determining a time fittingerror and a frequency fitting error for each sample of the firstplurality of the samples based on the constructed first instance of theparameter model; determining a combined fitting error for each sample ofthe first plurality of the samples based on the time fitting error andthe frequency fitting error of the corresponding sample; and classifyingeach sample of the first plurality of the samples as the inlier or theoutlier based on a comparison of the corresponding combined fittingerror to a fitting error threshold.
 14. The method of claim 9, whereinthe constructed first instance of the parameter model having a greatestnumber of inliers is selected.
 15. The method of claim 9, wherein theparameter model comprises a model of one of the following: a linearfrequency hopping sequence, a pseudo random frequency hopping sequences,a non-linear frequency hopping sequences, and an arbitrary linearfrequency hopping sequence.
 16. A non-transitory computer readablestorage medium having stored thereon instructions that, when executed,cause a computing device to: receive a set of samples for a timeinterval, the set of samples comprising samples of a first RF signaltransmitted between a first drone and a first drone controller, thefirst RF signal including a first frequency hopping sequence defined bya plurality of first frequency hopping parameters; obtain a parametermodel PM that represents the first frequency hopping sequencetransmitted between the first drone and the first drone controller, theparameter model comprising a plurality of model parameters respectivelymodelling the first frequency hopping parameters; fit the parametermodel to the set of samples by iterating over the following: randomlyselecting a first plurality of the samples from the set of samples,estimating values of the plurality of model parameters based on theselected first plurality of the samples, constructing a first instanceof the parameter model, the first instance of the parameter modelcomprising the estimated values of the plurality of model parameters,and classifying the first plurality of the samples as inliers oroutliers by evaluating fitting errors between the first plurality of thesamples and the constructed first instance of the parameter model;select one of the first instances of the parameter model based on theinliers and outliers for each of the constructed first instances of theparameter model; and identify the first drone based on the selectedfirst instance of the parameter model.
 17. The non-transitory computerreadable storage medium of claim 16, wherein the set of samples furthercomprises samples of a second RF signal transmitted between a seconddrone and a second drone controller, the second RF signal including asecond frequency hopping sequence defined by a plurality of secondfrequency hopping parameters, wherein the instructions, when executed,cause the at least one computing device to: fit the parameter model to asubset of the set of samples corresponding to the outliers that do notfit the selected first instance of the parameter model by iterating overthe following: randomly selecting a second plurality of samples from thesubset, estimating the values of the plurality of model parameters basedon the selected second plurality of samples, constructing a secondinstance of the parameter model based on the estimated values of theplurality of model parameters, and classifying the second plurality ofthe samples as inliers or outliers by evaluating fitting errors betweenthe second plurality of the samples and the second instance of theparameter model; select one of the second instances of the parametermodel based on the inliers and outliers for each of the constructedsecond instances of the parameter model; and identify the second dronebased on the selected second instance of the parameter model.
 18. Thenon-transitory computer readable storage medium of claim 16, wherein theestimating of the values of plurality of first frequency hoppingparameters is further based on a set of predefined channel centerfrequencies.
 19. The non-transitory computer readable storage medium ofclaim 18, wherein the plurality of first frequency hopping parameterscomprise a start time of a first hop, a hop period between twoneighboring hops, a start frequency of a first hop after an initialtime, a frequency difference between the two neighboring hops, and anumber of channels.
 20. The non-transitory computer readable storagemedium of claim 16, wherein the classifying of the first plurality ofthe samples as inliers or outliers comprises: determine a time fittingerror and a frequency fitting error for each sample of the firstplurality of the samples based on the constructed first instance of theparameter model; determine a combined fitting error for each sample ofthe first plurality of the samples based on the time fitting error andthe frequency fitting error of the corresponding sample; and classifyeach sample of the first plurality of the samples as the inlier or theoutlier based on a comparison of the corresponding combined fittingerror to a fitting error threshold.